Image recognition method and system based on deep learning

ABSTRACT

An image recognition method and system based on deep learning are provided. The image recognition system includes a first recognizing engine, at least one second recognizing engine and a processing circuit. The second recognizing engine is activated to recognize a testing image when the first recognizing engine is recognizing the testing image. The processing circuit determines whether to interrupt the first recognizing engine recognizing the testing image according to a result outputted by the second recognizing engine after the second recognizing engine completes recognition of the testing image.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This is a continuation application of U.S. application Ser. No.16/898,586, filed on Jun. 11, 2020 and entitled “IMAGE RECOGNITIONMETHOD AND SYSTEM BASED ON DEEP LEARNING”, which is a continuationapplication of U.S. application Ser. No. 15/670,511, filed on Aug. 7,2017 and entitled “IMAGE RECOGNITION METHOD AND SYSTEM BASED ON DEEPLEARNING”, and the entire contents of which are incorporated herein byreference.

FIELD OF THE DISCLOSURE

The present disclosure relates to an image recognition method andsystem, and more particularly to an image recognition method and systembased on deep learning.

BACKGROUND OF THE DISCLOSURE

Deep learning is a division of machine learning, and originates fromresearches on artificial neural network. Basically, deep learning is ahigh-level abstract algorithm employing multilayer processing, whichincludes complex hierarchy or multiple nonlinear transformations.Therefore, deep learning is widely used in machine vision, speechrecognition, natural language processing, audio recognition andbioinformatics.

Among the developed deep learning algorithms, deep convolutional neuralnetwork (CNN) is the one most used for image recognition. However,current models of deep convolutional neural network algorithm are mostlybuilt and trained with high resolution images. In practice, however, thetesting images are frequently in low resolution due to environmental orimage sensor issues, thus affecting the accuracy of image recognition.Therefore, a technical solution to improve the aforementioned limitationis necessary.

SUMMARY OF THE DISCLOSURE

The object of the present disclosure is to provide an image recognitionmethod based on deep learning, which includes the following steps:capturing a testing image by an image sensor; categorizing the testingimage into a high resolution mode or a low resolution mode by acategorizing engine according to at least one parameter used by theimage sensor when capturing the testing image; recognizing the testingimage by a first recognizing engine when the testing image iscategorized to the high resolution mode, and recognizing the testingimage by a second recognizing engine when the testing image iscategorized to the low resolution mode. In particular, the firstrecognizing engine and the second recognizing engine employnon-identical learning algorithms.

Preferably, the at least one parameter used by the image sensor whencapturing the testing image includes a gain value or an exposure time,and the categorizing engine is a context-aware engine.

In order to achieve the aforementioned objects, according to anembodiment of the present disclosure, an image recognition system basedon deep learning includes an image sensor, a categorizing engine, afirst recognizing engine and a second recognizing engine. The imagesensor captures a testing image. The categorizing engine categorizes thetesting image into a high resolution mode or a low resolution modeaccording to at least one parameter used by the image sensor whencapturing the testing image. The first recognizing engine recognizes thetesting image when the testing image is categorized to the highresolution mode. The second recognizing engine recognizes the testingimage when the testing image is categorized to the low resolution mode.In particular, the first recognizing engine and the second recognizingengine employ non-identical learning algorithms.

Preferably, the at least one parameter used by the image sensor whencapturing the testing image includes a gain value or an exposure time,and the categorizing engine is a context-aware engine.

In the present disclosure, the image recognition method and system basedon deep learning categorizes the testing image into the high resolutionmode or the low resolution mode, and then the testing image isrecognized by the appropriate recognizing engine. Therefore, the presentdisclosure effectively increases the accuracy of image recognition undervarious conditions.

In order to further the understanding regarding the present disclosure,the following embodiments are provided along with illustrations tofacilitate the understanding of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic diagram of an image recognition method based ondeep convolutional neural network algorithm according to an embodimentof the present disclosure;

FIG. 2 shows a schematic flowchart of an image recognition method basedon deep learning according to an embodiment of the present disclosure;

FIG. 3 shows a schematic block diagram of an image recognition systembased on deep learning according to an embodiment of the presentdisclosure;

FIG. 4 shows a schematic block diagram of an image recognition systembased on deep learning according to another embodiment of the presentdisclosure;

FIG. 5 shows a schematic flowchart of an image recognition method basedon deep learning according to an embodiment of the present disclosure;

FIG. 6 shows a schematic block diagram of an image recognition systembased on deep learning according to another embodiment of the presentdisclosure;

FIG. 7 shows a schematic flowchart of an image recognition method basedon deep learning according to an embodiment of the present disclosure;

FIG. 8 shows a schematic block diagram of an image recognition systembased on deep learning according to another embodiment of the presentdisclosure;

FIG. 9 shows a schematic flowchart of an image recognition method basedon deep learning according to an embodiment of the present disclosure;and

FIG. 10 shows a schematic flowchart of an image recognition method basedon deep learning according to an embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The aforementioned illustrations and following detailed descriptions areexemplary for the purpose of further explaining the scope of the presentinvention. Other objectives and advantages related to the presentinvention will be illustrated in the subsequent descriptions andappended drawings.

FIG. 1 shows a schematic diagram of an image recognition method based ondeep convolutional neural network algorithm according to an embodimentof the present disclosure. It should be noted that the followingdescriptions are based on the present embodiment shown in FIG. 1, butthe present disclosure is not limited thereto. A person having ordinaryskill in the art may modify the architecture of deep learning to fitparticular needs. In addition, details of the deep convolutional neuralnetwork algorithm are not addressed herein as it should have been knownto a person having ordinary skill in the art.

A recognizing engine employing deep convolutional neural networkalgorithm and trained with high resolution sample images cannotaccurately recognize a testing image with low resolution because themodeling and feature groups are built on high resolution images.Similarly, a recognizing engine trained with low resolution sampleimages cannot accurately recognize a testing image with high resolutionbecause the modeling and feature groups are built on low resolutionimages.

In addition, training a recognizing engine, which employs deepconvolutional neural network algorithm, with both high and lowresolution images could lead to a massive, overcomplicated model. Themodeling process would not converge easily and may fail to generate thefeature groups. Therefore, a person having ordinary skill in the artcould understand that the image recognition method in the presentdisclosure resolves the aforementioned limitation by separately traininga first recognizing engine with a first sample image group having highresolution and training a second recognizing engine with a second sampleimage group having low resolution separately. A first feature group isgenerated for high resolution images, and a second feature group isgenerated for low resolution images.

FIG. 2 shows a schematic flowchart of an image recognition method basedon deep learning according to the present embodiment. Step S203 iscategorizing the testing image as a high resolution mode or a lowresolution mode by a categorizing engine according to at least oneparameter used by an image sensor when capturing the testing image.Then, according to the result of categorization, taking step S205, whichis recognizing the testing image by a first recognizing engine when thetesting image is categorized to the high resolution mode, or taking stepS207, which is recognizing the testing image by a second recognizingengine when the testing image is categorized to the low resolution mode.The first recognizing engine and the second recognizing engine employnon-identical learning algorithms.

It should be noted that step S205 and S207 are in parallel and notmutually exclusive. In addition, the first recognizing engine isprovided with the first feature group when recognizing the testingimage. Similarly, the second recognizing engine is provided with thesecond feature group when recognizing the testing image. The details ofemploying a feature group by a recognizing engine when recognizing atesting image should be known to a person having ordinary skill in theart, and therefore is not described herein.

The image sensor could be a camera or a scanner for capturing a testingimage. The first recognizing engine could employ a deep convolutionalneural network algorithm, and the second recognizing engine could employa boosting algorithm. However, the present disclosure does not intend tolimit the type of image sensor and the algorithm employed by the firstand second recognizing engines.

Therefore, the core concept of the image recognition method in thepresent disclosure is to separately train a recognizing engine for highresolution images and a recognizing engine for low resolution images.The two recognizing engines generate two independent feature groups(i.e., two independent models) specifically for high resolution and lowresolution images respectively. A categorizing engine categorizes atesting image into the high resolution mode or the low resolution mode,and then the testing image is recognized by the appropriate recognizingengine. Therefore, the present disclosure increases the accuracy ofimage recognition under various environmental (or image resolution)conditions.

Specifically, the at least one parameter includes a gain value or anexposure time, and the categorizing engine is a context-aware engine.That is, the context-aware engine categorizes the testing image into thehigh resolution mode or the low resolution mode based on the gain valueor the exposure time. To be more specific, when the light sensed by theimage sensor is deficient, the image sensor would extend the time forlight sensing, whereby by observing that the exposure time to be greaterthan a first threshold, the image sensor would be determined to be in alow resolution mode. When the image sensor is overexposed, the imagesensor shortens the exposure time, in which when the exposure time isfound to be smaller than a second threshold, overexposure would thus bedetermined, and the image sensor would also be determined to be in a lowresolution mode. When the light sensed by the image sensor is moderate,the exposure time adjusted by the image sensor would fall within thefirst threshold and the second threshold, and the image sensor wouldtherefore be determined to be in a high resolution mode. the firstthreshold is smaller than the second threshold

Similarly, the gain value is the level of gain applied on the testingimage. A high gain value not only increases the intensity of the image,but also the intensity of the noise. Therefore, the categorizing enginecould categorize the testing image into the low resolution mode if thegain value of the testing image exceeds a second threshold. It should benoted that the present disclosure is not limited by the presentembodiment, in which the categorizing engine categorizes the testingimage based on exposure time, gain value or the combination thereof. Aperson having ordinary skill in the art could change the parameters usedby the categorizing engine (i.e., the context-aware engine) to fitparticular needs.

The present disclosure further provides an image recognition systembased on the aforementioned image recognition method. FIG. 3 shows aschematic block diagram of an image recognition system based on deeplearning according to an embodiment of the present disclosure. It shouldbe noted that the present disclosure is not limited by the embodimentshown in FIG. 3.

Specifically, the image recognition system 3 includes an image sensor31, a categorizing engine 33, a first recognizing engine 35 and a secondrecognizing engine 37, which are implemented in hardware or software. Inaddition, the image sensor 31, the categorizing engine 33, the firstrecognizing engine 35 and the second recognizing engine 37 could beimplemented integrally or separately, the present disclosure not beinglimited thereto. The first recognizing engine 35 is trained with a highresolution sample image group (not shown in drawings) and generates afirst feature group 30 a, and the second recognizing engine 37 istrained with a low resolution sample image group (not shown in drawings)and generates a second feature group 30 b.

It should be noted that the image recognition system 3 could conduct themethod shown in FIG. 2. That is, the image sensor 31 captures a testingimage T1, and the categorizing engine 33 categorizes the testing imageT1 to the high resolution mode or the low resolution mode according toat least one parameter used by the image sensor 31 when capturing thetesting image T1.

The first recognizing engine 35, which employs a deep convolutionalneural network algorithm, recognizes the testing image T1 when thetesting image T1 is categorized to the high resolution mode. The secondrecognizing engine 37, which employs a boosting algorithm, recognizesthe testing image T1 when the testing image T1 is categorized to the lowresolution mode. It should be noted that the algorithm employed by thefirst recognizing engine 35 is not limited to the deep convolutionalneural network and the algorithm employed by the second recognizingengine 37 is not limited to the boosting algorithm in the presentdisclosure.

As described previously, the at least one parameter used by the imagesensor 31 when capturing the testing image T1 includes a gain value oran exposure time, and the categorizing engine is a context-aware engine.A person having ordinary skill in the art should understand that thecore concept of the image recognition system in the present disclosureis to train a recognizing engine for high resolution images and arecognizing engine for low resolution image separately. The tworecognizing engines generate two independent feature groups (i.e., twoindependent models) specifically for high resolution and low resolutionimages respectively. The categorizing engine categorizes a testing imageinto the high resolution mode or the low resolution mode, and then thetesting image is recognized by the appropriate recognizing engine.Therefore, the present disclosure increases the accuracy of imagerecognition under various environmental (image resolution) conditions.

On the other hand, if the first recognizing engine 35 employs the deepconvolutional neural network algorithm shown in FIG. 1, a person havingordinary skill in the art should understand that the number of the firstfeature group 30 a increases as the layer of convolution increases whentraining the first recognizing engine with the first sample image group.Therefore, the image recognition system 3 should include a memory thatcould store a great number of the first feature group 30 a in order tobe successfully implemented in hardware. The present disclosure providesa solution to address the above issue.

FIG. 4 shows a schematic block diagram of an image recognition systembased on deep learning according to another embodiment of the presentdisclosure. In contrast to FIG. 3, the image recognition system 4 inFIG. 4 includes not only the image sensor 31, categorizing engine 33,the first recognizing engine 35 and the second recognizing engine 37,but also at least one codec 41. It should be noted that the followingembodiment includes a codec 41 a and a codec 41 b to facilitate theunderstanding of the image recognition system 4, but the number of codecin the present disclosure is not limited thereto. That is, the codec 41a and codec 41 b could be the same codec 41 or two different codecs, butthe present disclosure is not limited thereto. In addition, the codec 41a and codec 41 b could be implemented in hardware or software, but thepresent disclosure is not limited thereto.

Specifically, the codec 41 a encodes the feature group 30 a, and thecodec 41 b encodes the feature group 30 b. The encoded first featuregroup 30 a′ and the encoded second feature group 30 b′ are stored in amemory 43 of the image recognition system 4. It should be noted that thepresent embodiment includes a memory 43 a and a memory 43 b tofacilitate the understanding of the image recognition system 4, but thenumber of memory in the present disclosure is not limited thereto. Thatis, the memory 43 a and memory 43 b could be the same memory 43 or twodifferent memories, but the present disclosure is not limited thereto.In addition, the memory 43 a and memory 43 b could be a flash memory,but the present disclosure is not limited thereto.

Referring to FIG. 4, for example, the codec 41 a encodes the firstfeature group 30 a, and the encoded first feature group 30 a′ is storedin the memory 43 a. Similarly, the codec 41 b encodes the second featuregroup 30 b, and the encoded second feature group 30 b′ is stored in thememory 43 b. It should be noted that the present disclosure does notintend to limit the encoding method used by the codec 41 a and codec 41b when encoding the first feature group 30 a and second feature group 30b. A person having ordinary skill in the art may employ different codecsto fit particular needs. In this regard, the image recognition system 4in the present embodiment could effectively resolve the conventionalneed for storage of a great number of feature groups, and reduce thecost of hardware of the image recognition system 4.

Referring to FIG. 3 again, when the testing image T1 is categorized tothe high resolution mode, the first feature group 30 a is provided tothe first recognizing engine 35 for recognizing the testing image T1.Applying the same process in the image recognition system shown in FIG.4, when the testing image T1 is categorized to the high resolution mode,the image recognition system 4 decodes the encoded first feature group30 a′ stored in the memory 43 a by the codec 41 a, and the first featuregroup 30 a is provided to the first recognizing engine 35 forrecognizing the testing image T1.

Similarly, when the testing image T1 is categorized to the lowresolution mode, the image recognition system 4 decodes the encodedsecond feature group 30 b′ stored in the memory 43 b by the codec 41 b,and the second feature group 30 b is provided to the first recognizingengine 37 for recognizing the testing image T1. As described previously,the details of employing a feature group by a recognizing engine whenrecognizing a testing image should be known to a person having ordinaryskill in the art, and is not describe herein.

FIG. 5 shows a schematic flowchart of an image recognition method usingthe image recognition system 4 shown in FIG. 4, and the details of theimage recognition system 4 are not reiterated herein. Some of the stepsin FIG. 5 are identical to and use the same symbols as those in FIG. 2,and therefore are not reiterated herein.

Referring to FIG. 5, step S205 and step S207 further include stepsS501-S503 and steps S505-507, respectively. Step S501 is decoding theencoded first feature group stored in the memory by the codec when thetesting image is categorized to the high resolution mode. Step S503 isproviding the first feature group to the first recognizing engine forrecognizing the testing image. Step S505, on the other hand, is decodingthe encoded second feature group stored in the memory by the codec whenthe testing image is categorized to the low resolution mode. Step S507is providing the second feature group to the first recognizing enginefor recognizing the testing image.

Therefore, the core concept of the method and system of imagerecognition based on deep learning in the present disclosure is to traina recognizing engine for high resolution images and a recognizing enginefor low resolution images separately. The two recognizing enginesgenerate two independent feature groups specifically for high resolutionand low resolution images respectively. The context-aware engine (i.e.,the categorizing engine 33) categorizes a testing image into the highresolution mode or the low resolution mode, and then the testing imageis recognized by the appropriate recognizing engine. Therefore, thepresent disclosure increases the accuracy of image recognition undervarious conditions. In addition, the present disclosure further includesa codec to encode the feature groups generated by the recognizingengines after training so that the memory does not have to store a greatnumber of feature groups. Therefore, the present disclosure reduces costof hardware of the image recognition system.

The following embodiment is another example showing how the presentdisclosure reduces the cost of hardware of the image recognition systemby including a codec. FIG. 6 shows a schematic block diagram of an imagerecognition system based on deep learning according to anotherembodiment of the present disclosure. In contrast to FIG. 4, the imagerecognition system 6 in FIG. 6 includes at least one codec 61 and arecognizing engine 63. The recognizing engine is trained with a sampleimage group (not shown in drawings) and generates a feature group 60,which corresponds to the sample image group.

The codec 61 encodes the feature group 60, and the encoded feature group60′ is stored in at least one memory 65. The recognizing engine 63recognizes a testing image T1. When recognizing the testing image T1,the image recognition system 6 decodes the encoded first feature group60′ stored in the memory 65 by the codec 61, and the first feature group60 is provided to the recognizing engine 63 for recognizing the testingimage T1. It should be noted that the recognizing engine 63 could employa deep convolutional neural network algorithm, but the presentdisclosure is not limited thereto.

FIG. 7 shows a schematic flowchart of an image recognition method usingthe image recognition system 6 shown in FIG. 6, and the details of theimage recognition system 6 are not reiterated herein.

Step S701 is encoding the feature group by at least one codec, andstoring the encoded feature group in at least one memory. Step S703 isdecoding the encoded feature group stored in the at least one memory bythe at least one codec when recognizing a testing image, and thenproviding the feature group to the recognizing engine for recognizingthe testing image.

When the recognizing engine 63 employs the deep convolutional neuralnetwork algorithm, as shown in FIG. 1, a person having ordinary skill inthe art should understand that the time span for recognizing the testingimage T1 increases as the layer of convolution increases. Therefore, theimage recognition system 6 has to undergo a time-consuming andcomputation-intensive process to determine if the testing image T1includes a specific element by the recognizing engine employing the deepconvolutional neural network algorithm. The present disclosure providesa solution to address the above issue.

FIG. 8 shows a schematic block diagram of an image recognition systembased on deep learning according to another embodiment of the presentdisclosure. Some of the elements in FIG. 8 are identical to and use thesame symbols as FIGS. 3, 4 and 6, and therefore are not reiteratedherein. In contrast to FIG. 6, the image recognition system 8 in FIG. 8includes an image sensor 81, a first recognizing engine 83, at least onesecond recognizing engine 85 and a processing circuit 87. It should benoted that the image sensor 81, the first recognizing engine 83, the atleast one second recognizing engine 85 and the processing circuit 87could be implemented in hardware, or implemented in the combination ofhardware and software. The present disclosure does not intend to limitthe implementation of the elements in the image recognition system 8. Inaddition, the image sensor 81, the first recognizing engine 83, the atleast one second recognizing engine 85 and the processing circuit 87could be implemented integrally or separately, the present disclosurenot being limited thereto.

The present embodiment includes one second recognizing engine 85 tofacilitate the understanding of the image recognizing system 8, but thepresent disclosure is not limited thereto. Specifically, the imagesensor 81 captures a testing image T1, and the testing image T1 isrecognized by the first recognizing engine 83. The image sensor 81 couldbe a camera or a scanner, but the present disclosure is not limitedthereto. In addition, the testing image T1 is also recognized by thesecond recognizing engine 85.

In particular, the image recognition system 8 activates the secondrecognizing engine 85 to recognize the testing image T1 when recognizingthe testing image T1 by the first recognizing engine 83. The processingcircuit 87 determines whether to interrupt the first recognizing engine83 recognizing the testing image T1 according to a result (not shown indrawings) outputted by the second recognizing engine 85 after completingthe recognition of the testing image T1.

Similar to the image recognition system 6 in FIG. 6, a person havingordinary skill in the art should understand that the image sensor 81 inFIG. 8 could be omitted. That is, the image recognition system 8 obtainsthe testing image T1 without the image sensor 81, and transmits thetesting image T1 to the first recognizing image 83 and the secondrecognizing engine 85 directly. In addition, the image recognitionsystem 8 could further include a codec (not shown in drawings) and amemory (not shown in drawings). The functions of the codec and memoryhave been addressed previously and are not reiterated herein.

In one exemplary configuration of the embodiment, the first recognizingengine 83 and the second recognizing engine 85 could both employ theconvolutional neural network algorithm, and the convolutional layer inthe first recognizing engine 83 is different from a convolutional layerin the second recognizing engine 85. That is, one feature groupgenerated by the training process could be provided to both the firstand second recognizing engines. In practice, the first recognizingengine could employ a deep convolutional neural network algorithm andthe second recognizing engine could employ a shallow convolutionalneural network algorithm.

In another exemplary configuration of the embodiment, the firstrecognizing engine 83 and the second recognizing engine 85 could employnon-identical machine learning algorithms. In practice, the firstrecognizing engine 83 could employ, for example, the deep convolutionalneural network algorithm, and the second recognizing engine couldemploy, for example, an algorithm having a feature extraction circuitand a classifier. It should be noted that the present disclosure doesnot intend to limit the implementation of the feature extraction circuitand the classifier, a person having ordinary skill in the art couldmodify the design to fit particular needs.

Obviously, both exemplary configurations indicate that the secondrecognizing engine 85 could complete the image recognition processfaster than the first recognizing engine 83. Therefore, the core conceptof the present embodiment is to screen the testing image T1 by theadditional second recognizing engine 85, and determine if the testingimage T1 includes the specific element by the processing circuit 87 ofthe image recognition system 8. If the result of the screening has shownthat the testing image T1 does not include the specific element, theprocessing circuit 87 interrupts the recognition of the testing image T1by the first recognizing engine 83. In this regard, the imagerecognition system 8 prevents unnecessary computation effort and time.

FIG. 9 shows a schematic flowchart of an image recognition method usingthe image recognition system 8 shown in FIG. 8, and the details of theimage recognition system 8 are not reiterated herein.

Step S901 is activating the second recognizing engine to recognize thetesting image when recognizing the testing image by the firstrecognizing engine. Step S903 is determining whether to interrupt therecognition of the testing image by the first recognizing engine by theprocessing circuit according to the result outputted by the secondrecognizing engine after recognition of the testing image by the secondrecognizing engine is completed.

It should be noted that the following describes how the processingcircuit determines whether to interrupt the recognition of the testingimage by the first recognizing engine. FIG. 10 shows a schematicflowchart of an image recognition method based on deep learningaccording to an embodiment of the present disclosure. Some of the stepsin FIG. 10 are identical to and use the same symbols as FIG. 9, andtherefore are not reiterated herein.

As described previously, the second recognizing engine could employ, forexample, a shallow convolutional neural network algorithm or analgorithm having a feature extraction circuit and a classifier.Therefore, the result outputted by the second recognizing engineindicates if the testing image includes a specific element. In practice,the result could be a probability value. In this case, a high value ofprobability indicates that the specific element is very likely to beincluded in the testing image.

In this regard, step S903 could further include steps S101-S105.Firstly, step S101 is determining if the result is greater than or equalto a threshold. Next, in step S103, the processing circuit sends aninterrupt command to interrupt the first recognizing engine fromrecognizing the testing image if the result is less than the threshold.Otherwise, in step S105, the processing circuit sends no interruptcommand to interrupt the first recognizing engine from recognizing thetesting image if the result is greater than or equal to the threshold.

In other words, the processing circuit interrupts the recognition of thetesting image by the first recognizing engine when the result is lessthan the threshold, which suggests that the probability of the specificelement being included in the testing image is low. This step preventsunnecessary computation effort and time. On the other hand, theprocessing circuit does not interrupt the recognition of the testingimage by the first recognizing engine when the result is greater than orequal to the threshold, which suggests that the probability of thespecific element being included in the testing image is high. The stepallows the highly accurate first recognizing engine, which employs thedeep convolutional neural network algorithm, to continue the recognitionof the testing image. The effect of the deep convolutional neuralnetwork algorithm has been described previously, and is thus notreiterated herein.

The descriptions illustrated supra set forth simply the preferredembodiments of the present invention; however, the characteristics ofthe present invention are by no means restricted thereto. All changes,alterations, or modifications conveniently considered by those skilledin the art are deemed to be encompassed within the scope of the presentinvention delineated by the following claims.

What is claimed is:
 1. An image recognition method based on deeplearning, including steps of: activating a second recognizing engine torecognize a testing image when the testing image is also recognized by afirst recognizing engine; and determining whether to interrupt the stepof recognizing the testing image by the first recognizing engine by aprocessing circuit according to a result outputted by the secondrecognizing engine after the step of recognizing the testing image bythe second recognizing engine is completed; wherein the firstrecognizing engine and the second recognizing engine employconvolutional neural network algorithms, the first recognizing engineemploys a deep convolutional neural network algorithm, and the secondrecognizing engine employs a shallow convolutional neural networkalgorithm.
 2. The image recognition method according to claim 1, whereinthe step of determining whether to interrupt the step of recognizing thetesting image by the first recognizing engine further includes:determining if the result is greater than or equal to a threshold by theprocessing circuit; and the processing circuit sending no interruptcommand to interrupt the step of recognizing the testing image by thefirst recognizing engine if the result is greater than or equal to thethreshold.
 3. An image recognition method based on deep learning,including steps of: activating a second recognizing engine to recognizea testing image when the testing image is also recognized by a firstrecognizing engine; and determining whether to interrupt the step ofrecognizing the testing image by the first recognizing engine by aprocessing circuit according to a result outputted by the secondrecognizing engine after the step of recognizing the testing image bythe second recognizing engine is completed; wherein the firstrecognizing engine and the second recognizing engine employnon-identical learning algorithms, the first recognizing engine employsa deep convolutional neural network algorithm, and the secondrecognizing engine employs a recognition algorithm having a featureextraction circuit and a classifier.
 4. The image recognition methodaccording to claim 3, wherein the step of determining whether tointerrupt the step of recognizing the testing image by the firstrecognizing engine further includes: determining if the result isgreater than or equal to a threshold by the processing circuit; and theprocessing circuit sending no interrupt command to interrupt the step ofrecognizing the testing image by the first recognizing engine if theresult is greater than or equal to the threshold.
 5. An imagerecognition system based on deep learning, comprising: an image sensorfor capturing a testing image; a first recognizing engine forrecognizing the testing image; at least one second recognizing enginefor recognizing the testing image, wherein the at least one secondrecognizing engine is activated to recognize the testing image when thefirst recognizing engine is recognizing the testing image; and aprocessing circuit for determining whether to interrupt the firstrecognizing engine recognizing the testing image according to a resultoutputted by the at least one second recognizing engine after the atleast one second recognizing engine completes recognition of the testingimage; wherein the first recognizing engine and the second recognizingengine employ convolutional neural network algorithms, the firstrecognizing engine employs a deep convolutional neural networkalgorithm, and the second recognizing engine employs a shallowconvolutional neural network algorithm.
 6. The image recognition systemaccording to claim 5, wherein the processing circuit conducts thefollowing steps to determine whether to interrupt the first recognizingengine recognizing the testing image: determining if the result isgreater than or equal to a threshold by the processing circuit; and theprocessing circuit sending no interrupt command to interrupt the firstrecognizing engine recognizing the testing image if the result isgreater than or equal to the threshold.
 7. An image recognition systembased on deep learning, comprising: an image sensor for capturing atesting image; a first recognizing engine for recognizing the testingimage; at least one second recognizing engine for recognizing thetesting image, wherein the at least one second recognizing engine isactivated to recognize the testing image when the first recognizingengine is recognizing the testing image; and a processing circuit fordetermining whether to interrupt the first recognizing enginerecognizing the testing image according to a result outputted by the atleast one second recognizing engine after the at least one secondrecognizing engine completes recognition of the testing image; whereinthe first recognizing engine and the second recognizing engine employnon-identical learning algorithms, the first recognizing engine employsa deep convolutional neural network algorithm, and the secondrecognizing engine employs a recognition algorithm having a featureextraction circuit and a classifier.
 8. The image recognition systemaccording to claim 7, wherein the processing circuit conducts thefollowing steps to determine whether to interrupt the first recognizingengine recognizing the testing image: determining if the result isgreater than or equal to a threshold by the processing circuit; and theprocessing circuit sending no interrupt command to interrupt the firstrecognizing engine recognizing the testing image if the result isgreater than or equal to the threshold.